{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9866666666666667\n"
     ]
    }
   ],
   "source": [
    "import sklearn.datasets as datasets\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# 抓取数据，当做样本\n",
    "# 鸢尾花样本\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "# 样本数据\n",
    "x_train = iris.data[::2]\n",
    "\n",
    "y_train = iris.target[::2]\n",
    "\n",
    "\n",
    "# 测试数据集\n",
    "x_test = iris.data[1::2]\n",
    "y_test = iris.target[1::2]\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(x_train, y_train)\n",
    "result = knn.predict(x_test)\n",
    "score = knn.score(x_test, y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4,\n       1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1. , 1.7, 1.9, 1.6,\n       1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.5, 1.3,\n       1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5,\n       4.9, 4. , 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4. , 4.7, 3.6,\n       4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4. , 4.9, 4.7, 4.3, 4.4, 4.8, 5. ,\n       4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4. , 4.4,\n       4.6, 4. , 3.3, 4.2, 4.2, 4.2, 4.3, 3. , 4.1, 6. , 5.1, 5.9, 5.6,\n       5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5. , 5.1, 5.3, 5.5,\n       6.7, 6.9, 5. , 5.7, 4.9, 6.7, 4.9, 5.7, 6. , 4.8, 4.9, 5.6, 5.8,\n       6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1,\n       5.9, 5.7, 5.2, 5. , 5.2, 5.4, 5.1])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x17bf33989e8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ms3atXUvV3OIFBdZ8ozlOlGigirsJsXkzPPaY3RmGM1PUFz172sjHysjJpCQ44wz48kt3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\nVhRFiTP+P6oDa0Xa6aMtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = ListedColormap([\"#FF0000\", \"#00FF00\", \"#0000FF\"])\n",
    "\n",
    "plt.scatter(iris.data[:, 1], iris.data[:, 2], c=iris.target, cmap=cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17bed2d5dd8>,\n <matplotlib.lines.Line2D at 0x17bf43ffbe0>,\n <matplotlib.lines.Line2D at 0x17bf43ffd30>,\n <matplotlib.lines.Line2D at 0x17bf43ffe80>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zFmcxhznYs+x8v+d7/tb2b1gtVsyYiaf4NNiDh5d4iYUspDGNGe95gZ3z27B6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\nUXAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(iris.data[:, 2], iris.data[:, 3], c=iris.target, cmap=cmap)\n",
    "plt.plot(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n           metric_params=None, n_jobs=1, n_neighbors=15, p=2,\n           weights='uniform')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = iris.data[:,:2]\n",
    "y = iris.target\n",
    "n = 15\n",
    "myknn = KNeighborsClassifier(n_neighbors=15)\n",
    "myknn.fit(x,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3\n8.9\n"
     ]
    }
   ],
   "source": [
    "xmin,xmax = x[:,0].min()-1,x[:,0].max()+1\n",
    "print(xmin)\n",
    "print(xmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n5.4\n"
     ]
    }
   ],
   "source": [
    "ymin,ymax = x[:,1].min()-1,x[:,1].max()+1\n",
    "print(ymin)\n",
    "print(ymax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(220, 280)\n(220, 280)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "#图片，x,y每一步的步长\n",
    "h = 0.02\n",
    "#生成网格\n",
    "xx,yy = np.meshgrid(np.arange(xmin,xmax,h),np.arange(ymin,ymax,h))\n",
    "print(xx.shape)\n",
    "print(yy.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#预测\n",
    "z = myknn.predict(np.c_[xx.ravel(),yy.ravel()])\n",
    "z = z.reshape(xx.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iUBBsj+/E6Wx/cyzU1peTwnL22sPD0vkFES+XoaBAmHJzObc9vI2KFeHdQvIr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\nQIDJTGYMjX4ibFQnOnEwB/M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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.rcParams['font.sas-serig']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "#显示背景颜色\n",
    "cmap_light = ListedColormap([\"#FFAAAA\",\"#AAFFAA\",\"#AAAAFF\"])\n",
    "plt.pcolormesh(xx,yy,z,cmap=cmap_light)\n",
    "#显示点的颜色\n",
    "cmap_bold = ListedColormap([\"#FF0000\",\"#00FF00\", \"#0000FF\"])\n",
    "plt.scatter(x[:,0],x[:,1], c=y,cmap=cmap_bold)\n",
    "#plt.xlim(xx.min(),xx.max())\n",
    "#plt.xlim(yy.min(),yy.max())\n",
    "plt.title(\"classify\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
